Meditation may help people see thoughts and experiences less as solid realities—a process called dereification—and this mental shift can be tracked in the brain using EEG. By creating personalized models of brain states from electrical activity, researchers identified distinct neural patterns that correspond to moments of dereification during meditation. These patterns suggest that the brain moves through different electrophysiological states as a person becomes less identified with their thoughts. The findings indicate that EEG can capture subtle, moment-to-moment changes in how the mind relates to experience, offering a way to measure a core aspect of mindfulness that was previously difficult to study objectively.
Meditation's benefits are increasingly recognized, but the brain's electrical activity during meditative states is not fully understood. Existing markers have limited predictive accuracy, suggesting important information is missing. This work converts EEG time series into scale-free networks using horizontal visibility graphs, which distinguish deterministic from random systems and model new aspects of brain oscillations. The authors introduce a class of network-based predictors that outperform popular spectral and nonlinear features like complexity or entropy. These predictors show statistical significance for several meditation types, using data from highly skilled meditators, and are suitable for real-time analysis and applications such as neurofeedback.